Comments (12)
have you anyone get the fig2 result in paper? my model doesn't convergence。
I hava the same question, what's your specific condition? When i train the model, the reward almost don't change. When i test the TMs, i find the training as if never learned sth.
from a-deep-rl-approach-for-sdn-routing-optimization.
have you anyone get the fig2 result in paper? my model doesn't convergence。
I hava the same question, what's your specific condition? When i train the model, the reward almost don't change. When i test the TMs, i find the training as if never learned sth.
yes, learn nothing, but you should fix the TM when testing over the training stage
from a-deep-rl-approach-for-sdn-routing-optimization.
have you anyone get the fig2 result in paper? my model doesn't convergence。
I hava the same question, what's your specific condition? When i train the model, the reward almost don't change. When i test the TMs, i find the training as if never learned sth.
yes, learn nothing, but you should fix the TM when testing over the training stage
Sorry to bother you, i don't really understand what u mean? How to fix the TMs?
from a-deep-rl-approach-for-sdn-routing-optimization.
have you anyone get the fig2 result in paper? my model doesn't convergence。
I hava the same question, what's your specific condition? When i train the model, the reward almost don't change. When i test the TMs, i find the training as if never learned sth.
yes, learn nothing, but you should fix the TM when testing over the training stage
Sorry to bother you, i don't really understand what u mean? How to fix the TMs?
the author's code didn't do testing , so you have to write test code by yourself to get the fig result in paper.
from a-deep-rl-approach-for-sdn-routing-optimization.
have you anyone get the fig2 result in paper? my model doesn't convergence。
Sorry to bother you!
Have You get the Fig. 1 result ? I still can't understand how to use the TMs mentioned in the paper to train this DRL-Agent . Can you explain the whole training process, because in the given code , I did not find any correlation between the previous state and the new state, It seems that they are all randomly generated using np.random.
from a-deep-rl-approach-for-sdn-routing-optimization.
have you anyone get the fig2 result in paper? my model doesn't convergence。
Sorry to bother you!
Have You get the Fig. 1 result ? I still can't understand how to use the TMs mentioned in the paper to train this DRL-Agent . Can you explain the whole training process, because in the given code , I did not find any correlation between the previous state and the new state, It seems that they are all randomly generated using np.random.
Excuse me. Also have similar question, I can't understand why the state(TMs) and the new_state(TMs) are randomly generated in the step function which in Environment.py .It isn't meeting the logic of DRL.
from a-deep-rl-approach-for-sdn-routing-optimization.
Can any get the result of the same as in paper as the model is not converging
from a-deep-rl-approach-for-sdn-routing-optimization.
I have the same question. I dont't understand why the old state and the new state are randomly generated in the Environment.py.
from a-deep-rl-approach-for-sdn-routing-optimization.
from a-deep-rl-approach-for-sdn-routing-optimization.
I have the same question. I dont't understand why the old state and the new state are randomly generated in the Environment.py.
Did you run the whole simulations or not.
…
Excuse me. I run the whole simulations. But in my daily study, the STATE of Reinforcement Learning is usually changed by the ACTION, but in the code of this paper, we can find flie that in Environment.py, its NEW STATE and OLD STATE are randomly generated, which does not seem to meet the logic of Reinforcement Learning. Which teacher can answer my confusion? Thank you very much.
from a-deep-rl-approach-for-sdn-routing-optimization.
I have the same question. I dont't understand why the old state and the new state are randomly generated in the Environment.py.
Did you run the whole simulations or not.
…Excuse me. I run the whole simulations. But in my daily study, the STATE of Reinforcement Learning is usually changed by the ACTION, but in the code of this paper, we can find flie that in Environment.py, its NEW STATE and OLD STATE are randomly generated, which does not seem to meet the logic of Reinforcement Learning. Which teacher can answer my confusion? Thank you very much.
I've also found this question. I think that the author need to do some explanations. It disobey the basic logic of reinforcement learning. @gissimo
from a-deep-rl-approach-for-sdn-routing-optimization.
hello,Please ask how I can run the whole simulation, can you tell me the approximate steps, thank you very much!
from a-deep-rl-approach-for-sdn-routing-optimization.
Related Issues (16)
- How to assign the parameters during execute your code!! HOT 2
- Getting an error : No such file or directory HOT 2
- Can't Run 'make' from inside 'omnet/router'. HOT 2
- Can this code run under windows?
- Cannot run the ddpg.py !!! HOT 2
- FileNotFoundError: [Errno 2] No such file or directory: 'runs//t1677080026736617/Delay.txt'
- how to start train
- Need Guideline
- How to run the code HOT 3
- FileNotFoundError: [Errno 2] No such file or directory: 'omnet/router/networkRL' HOT 1
- Cannot find the weight HOT 2
- error during training
- How to generate traffic matrices? HOT 4
- How long it will take to run the program.
- Regarding tensorflow version change
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from a-deep-rl-approach-for-sdn-routing-optimization.